The present disclosure relates to an image processing apparatus and an image processing method. Specifically, the present disclosure relates to an image processing apparatus and an image processing method performing cyclic noise reduction processing by using previous-frame output image data subjected to data compression/decompression processing.
Recently, in image processing LSIs (Large Scale Integration) and the like, complex high-image-quality functions and the like increase memory bandwidths greatly. As a countermeasure, in many cases, communication volumes with external memories are reduced by compressing image data.
FIG. 4 shows an example of an image processing apparatus 200 performing cyclic noise reduction processing. The image processing apparatus 200 includes a noise reduction unit 210, an encoder 220, a frame memory 230, and a decoder 240. In the image processing apparatus 200, image data temporarily stored in the frame memory 230 is handled as compressed image data, and thus data communication volume between the noise reduction unit 210 side and the frame memory 230 is reduced.
Output image data output from the noise reduction unit 210 is supplied to the encoder 220. The encoder 220 performs invertible data compression processing on the output image data by using a predetermined compression algorithm to thereby obtain compressed image data. The compressed image data is temporarily stored in the frame memory 230 constituting delay means.
Previous-frame compressed image data is read from the frame memory 230 and supplied to the decoder 240. The decoder 240 performs data decompression processing on the compressed image data to thereby obtain previous-frame output image data. In addition to the previous-frame output image data, input image data is supplied to the noise reduction unit 210.
The noise reduction unit 210 calculates a noise reduction processing amount (NR processing amount) as a feedback value based on the input image data and the previous-frame output image data. Further, the noise reduction unit 210 adds the NR processing amount to the input image data to thereby obtain output image data.
In the configuration of the image processing apparatus 200 shown in FIG. 4, the image data compression is performed on a frame basis, which is a spatial-directional compression. Meanwhile, the noise reduction is time-directional processing. The processing dimensions are different from each other. Because of this, a noise reduction feedback ratio (NR feedback ratio) is 100%, and previous-frame output image data may be output continuously.
For example, a noise reduction of adding differential data (frame differential value) between previous-frame output image data (image data of previous frame) and input image data (image data of current frame) to input image data at a feedback ratio of 50% is assumed.
In the noise reduction, as shown in (a) in FIG. 5, it is assumed that an input image data level of a certain pixel is changed from “8” in the Nth frame to “0” in the (N+1)th frame. In this case, as shown in (d) in FIG. 5, output image data converges to the input image data level in the (N+4)th frame. Note that (b) in FIG. 5 shows previous-frame output image data, which is one-frame-delayed output image data shown in (d) in FIG. 5. Further, (c) in FIG. 5 shows a frame differential value, which is a value obtained by subtracting the input image data shown in (a) in FIG. 5 from the previous-frame output image data shown in (b) in FIG. 5.
It is assumed that the following compression function is combined with the noise reduction of the image processing apparatus 200 of FIG. 4. In the compression function, 8-bit image data is compressed into 5-bit image data by rounding off the 3rd bit from the LSB (Least Significant Bit) side. In this case, as shown in (d) in FIG. 6, when the output image data level reaches 4, the noise reduction processing is buried in compression errors, and the output image data may not converge on the input image data permanently. This is the 100% NR feedback ratio state.
Note that, similar to (a) in FIG. 5, (a) in FIG. 6 shows an input image data level of a certain pixel, and the input image data level is changed from “8” in the Nth frame to “0” in the (N+1)th frame. (b) in FIG. 6 shows previous-frame output image data subjected to data compression/decompression processing. Since the previous-frame output image data is subjected to data compression/decompression processing, it is different from the previous-frame output image data, which is merely one-frame-delayed output image data, shown in (b) in FIG. 5.
That is, in the above-mentioned compression algorithm, image data is compressed into 5-bit image data by rounding off the 3rd bit from the LSB side. Therefore, the output image data level “4” may not be expressed in compressed image data, and “8” is thus written in the frame memory 230. As a result, the previous-frame output image data shown in (b) in FIG. 6 has a level “8” in the (N+2)th frame and thereafter.
Further, (c) in FIG. 6 shows a frame differential value, which is a value obtained by subtracting the input image data shown in (a) in FIG. 6 from the previous-frame output image data shown in (b) in FIG. 6. The previous-frame output image data shown in (b) in FIG. 6 is subjected to data compression/decompression processing, and thus has a level “8” in the (N+2)th frame and thereafter, as described above. As a result, the frame differential value is “8” in the (N+1)th frame and thereafter. Therefore, as described above, when the output image data level reaches 4, the noise reduction processing is buried in compression errors. As a result, the output image data may not converge on the input image data permanently. The NR feedback ratio is 100%, that is, the output image data is held as it is.
Because of the above-mentioned state where the NR feedback ratio is 100%, an output image includes a faint afterimage of the previous frame. FIG. 7 shows examples of images of input image data (input images) and images of output image data (output images). In the examples, rectangular inserted image portions IM are image portions having high loss information amounts due to compression. In the image portions IM, because of data compression/decompression processing, the NR feedback ratio is 100%. As a result, in the output images, the image portion IM includes a faint afterimage of the previous frame image.
For example, Japanese Patent Application Laid-open H03-266565 (hereinafter, referred to as Patent Document 1) describes that, in a case of a scene change and the like in which a differential between a present image and a previous image is large, in order to shorten a display time of an afterimage of a previous image, a NR feedback ratio is temporarily reduced, and an output image is converged on an input image at high speed.
As described above, in order to avoid the state where the NR feedback ratio is 100% and an output image includes an afterimage, a NR feedback ratio may be reduced periodically. In this case, the noise reduction is ineffective periodically. As a result, a noise repeatedly goes off, then comes out, then goes off, and then comes out. In an image including a noise, the noise reduction effect is reduced.
It is desirable to preferably avoid the state where a NR feedback ratio is 100% and an output image includes an afterimage, in a case of performing cyclic NR (noise reduction) processing by using previous-frame output image data subjected to data compression/decompression processing.